Mining Patterns of Drug-Disease Association from Biomedical Texts
Pages 84 - 90
Abstract
Drug repurposing aims to identify new indications for approved drugs, and it can promisingly reduce time and drug development costs. The goal of the paper, drug-disease relation extraction automatically from biomedical texts, is fundamental to the study of drug repurposing since lots of clinical case studies published in an unstructured textual form. To analyze the number of verbs and nouns pertinent to diseases and medications in the training data, two models with different drug-disease orders are established, and some rules are proposed at this phase. The first model is for the sentences with the order that the disease name precedes the drug name. The second model is for the reverse order to the first model. These verbs and nouns are then classified into categories of "pure association," "pure no association" and "neutrals." Among them, some neutrals are further verified by the Chi-square test method. As a result, the associations between diseases and medications are identified, which are called patterns later. Finally, the patterns are used in the test data to extract the disease and drug pairs. The best experimental results show the precision value of 100%, recall value of 89.0%, and F-score value of 94.2%.
References
[1]
Islamaj Dogan, R., Murray, G. C., Névéol, A., and Lu, Z. 2009. Understanding ® user search behavior through log analysis. Database, 2009, bap018.
[2]
Salleras Redonnet, M. and Quintana Codina, M. 2016. Ingenolmebutate gel for the treatment of Bowen's disease: a case report of three patients. Dermatol. Ther. 29, 4 (Jul. 2016), 236--239.
[3]
Jeffe, J. S. and Sulman, C. G. 2015. The use of botulinum toxin B in the treatment of a post-traumatic sialocele in a 4-year-old child: A case report. Int. J. Pediatr. Otorhinolaryngol. 79, 12 (Dec. 2015), 2446--2449.
[4]
Chiang, A. P. and Butte, A. J. 2009. Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin. Pharmacol. Ther. 86, 5 (Nov. 2009), 507--510.
[5]
Gottlieb, A., Stein, G. Y., Ruppin, E., and Sharan, R. 2011. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 7, 1 (Jun. 2011), 496.
[6]
Martínez, V., Navarro, C., Cano, C., Fajardo, W., and Blanco, A. 2015. DrugNet: Network-based drug--disease prioritization by integrating heterogeneous data. Artif. Intell. Med. 63, 1 (Jan. 2015), 41--49.
[7]
Wang, W., Yang, S., Zhang, X., and Li, J. 2014. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 30, 20 (Oct. 2014), 2923--2930.
[8]
Chen, E. S., Hripcsak, G., Xu, H., Markatou, M., and Friedman, C. 2008. Automated acquisition of disease--drug knowledge from biomedical and clinical documents: an initial study. J. Am. Med. Inform. Assoc. 15, 1 (Jan. 2008), 87--98.
[9]
Jang, D., Lee, S., Lee, J., Kim, K., and Lee, D. 2016. Inferring new drug indications using the complementarity between clinical disease signatures and drug effects. J. Biomed. Inform. 59 (Feb. 2016), 248--257.
[10]
Wren, J. D. 2004. Extending the mutual information measure to rank inferred literature relationships. BMC Bioinformatics. 5 (Oct. 2004), 145.
[11]
Bundschus, M., Dejori, M., Stetter, M., Tresp, V., and Kriegel, H. P. 2008. Extraction of semantic biomedical relations from text using conditional random fields. BMC Bioinformatics, 9 (Apr. 2008), 207.
[12]
Buyko, E., Beisswanger, E., and Hahn, U. (2012). The extraction of pharmacogenetic and pharmacogenomic relations--a case study using PharmGKB. In Proceedings of Pacific Symposium on Biocomputing (Hawaii, USA, 3-7 January 03-07, 2012). 376--87.
[13]
Hahn, U., Buyko, E., Landefeld, R., Mühlhausen, M., Poprat, M., Tomanek, K., and Wermter, J. 2008. An overview of JCoRe, the JULIE lab UIMA component repository. In Proceedings of the LREC 2008 Workshop 'Towards Enhanced Interoperability for Large HLT Systems - UIMA for NLP'(Marrakech, Morocco, May 31, 2008). European Language Resources Association (ELRA), 8, 1--7.
[14]
Rosario, B. and Hearst, M. A. 2004. Classifying semantic relations in bioscience texts. In Proceedings of the 42nd annual meeting on association for computational linguistics (Barcelona, Spain, July 21-26, 2004). Association for Computational Linguistics Stroudsburg, PA, USA, 430--437.
[15]
Abacha, A. B. and Zweigenbaum, P. 2011. Automatic extraction of semantic relations between medical entities: a rule based approach. J. Biomed. Semant. 2, Suppl 5 (Oct. 2011), S4.
[16]
Gopalakrishnan, V., Lustgarten, J. L., Visweswaran, S., and Cooper, G. F. 2010. Bayesian rule learning for biomedical data mining. Bioinformatics, 26, 5 (Jan. 2010), 668--675.
[17]
Jung, J. and Lee, D. 2013. Inferring disease association using clinical factors in a combinatorial manner and their use in drug repositioning. Bioinformatics, 29, 16 (Aug. 2013), 2017--2023.
[18]
Xu, R. and Wang, Q. 2013. Large-scale extraction of accurate drug-disease treatment pairs from biomedical literature for drug repurposing. BMC Bioinformatics, 14 (Jun. 2013), 181.
[19]
Hu, G. and Agarwal, P. 2009. Human disease-drug network based on genomic expression profiles. PloS One, 4, 8 (Aug. 2009), e6536.
[20]
Zhao, S. and Li, S. 2012. A co-module approach for elucidating drug--disease associations and revealing their molecular basis. Bioinformatics, 28, 7 (Apr. 2012), 955--961.
[21]
Von Eichborn, J., Murgueitio, M. S., Dunkel, M., Koerner, S., Bourne, P. E., and Preissner, R. 2011. PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res. 39 (Jan. 2011), D1060-D1066.
[22]
Yang, L. and Agarwal, P. 2011. Systematic drug repositioning based on clinical side-effects. PloS One, 6, 12 (Dec. 2011), e28025.
[23]
Névéol, A. and Lu, Z. 2010. Automatic integration of drug indications from multiple health resources. In Proceedings of the 1st ACM International Health Informatics Symposium (Arlington, VA, USA, November 11-12, 2010). IHI '10. ACM, New York, NY, 666--673.
[24]
Qu, X. A., Gudivada, R. C., Jegga, A. G., Neumann, E. K., and Aronow, B. J. 2009. Inferring novel disease indications for known drugs by semantically linking drug action and disease mechanism relationships. BMC Bioinform. 10, Suppl. 5 (May 2009), S4.
[25]
Zhu, Q., Freimuth, R. R., Pathak, J., Durski, M. J. and Chute, C. G. 2013. Disambiguation of PharmGKB drug--disease relations with NDF-RT and SPL. J. Biomed. Inform. 46, 4 (Aug. 2013), 690--696.
[26]
McHugh, M. L. 2013. The Chi-square test of independence. Biochem. Med. 23, 2 (June 2013), 143--149.
Index Terms
- Mining Patterns of Drug-Disease Association from Biomedical Texts
Recommendations
Discovering Informative Syntactic Relationships between Named Entities in Biomedical Literature
DBKDA '10: Proceedings of the 2010 Second International Conference on Advances in Databases, Knowledge, and Data ApplicationsThe discovery of new and potentially meaningful relationships between named entities in biomedical literature can take great advantage from the application of multirelational data mining approaches in text mining. This is motivated by the peculiarity of ...
A Highly Efficient Biomolecular Network Representation Model for Predicting Drug-Disease Associations
Intelligent Computing MethodologiesAbstractIdentification of drug-disease association is crucial for drug development and reposition. However, discovering drugs which are associated with diseases from in vitro testing is costly and time-consuming. Accumulating evidence showed that ...
Identification of Conclusive Association Entities by Biomedical Association Mining
Intelligent Information and Database SystemsAbstractConclusive association entities (CAEs) in the title and the abstract of an article a are those biomedical entities (e.g., genes, diseases, and chemicals) that are specific targets on which conclusive findings about their associations are reported ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
January 2018
164 pages
ISBN:9781450353410
DOI:10.1145/3180382
Copyright © 2018 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
In-Cooperation
- RIED, Tokai Univ., Japan: RIED, Tokai University, Japan
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 18 January 2018
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICBBB 2018
ICBBB 2018: 2018 8th International Conference on Bioscience, Biochemistry and Bioinformatics
January 18 - 20, 2018
Tokyo, Japan
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 89Total Downloads
- Downloads (Last 12 months)2
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in